Narrow window data-independent acquisition on the Orbitrap Astral Mass Spectrometer enables fast and deep coverage of the plasma glycoproteome
Bibliographic record
Abstract
Abstract Recently, a conceptually new mass analyzer was introduced by pairing a quadrupole Orbitrap mass spectrometer with an asymmetric track lossless (Astral ™ ) analyzer. This system provides >200-Hz MS/MS scanning speed, high resolving power and sensitivity, and low-ppm mass accuracy. This instrument allows a narrow-window data-independent (nDIA) strategy, improving sensitivity and reproducibility even when using very short LC gradients. Although this represents a new technical milestone in peptide-centric proteomics, this new system has not yet been evaluated for the analyses of very complex and clinically important proteomes, such as represented by the plasma glycoproteome. Here, we evaluated the Orbitrap Astral mass spectrometer for the analysis of the plasma glycoproteome, and pioneer a dedicated nDIA workflow, themed nGlycoDIA. With substantially adjusted parameters and varying collision energies, nGlycoDIA has clear benefits for plasma glycoproteomics. We tested our method both in glycopeptide enriched and crude plasma, leading to the identification of more than 3000 unique glycoPSMs from 181 glycoproteins, covering a dynamic range of 7 orders of magnitude in the enriched plasma sample in just 40 minutes. In addition, we detect for the first time several glycosylated cytokines that have a reported plasma concentration in the ng/L range. Furthermore, shortening the gradient to 10 min still allows the detection of almost 2500 unique glycoPSMs from enriched plasma, indicating that high-throughput indepth clinical plasma glycoproteomics may be within reach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".